Mobile manipulators typically encounter significant challenges in navigating narrow, cluttered environments due to their high-dimensional state spaces and complex kinematics. While reactive methods excel in dynamic settings, they struggle to efficiently incorporate complex, coupled constraints across the entire state space. In this work, we present a novel local reactive controller that reformulates the time-domain single-step problem into a multi-step optimization problem in the spatial domain, leveraging the propagation of a serial kinematic chain. This transformation facilitates the formulation of customized, decoupled link-specific constraints, which is further solved efficiently with augmented Lagrangian differential dynamic programming (AL-DDP). Our approach naturally absorbs spatial kinematic propagation in the forward pass and processes all link-specific constraints simultaneously during the backward pass, enhancing both constraint management and computational efficiency. Notably, in this framework, we formulate collision avoidance constraints for each link using accurate geometric models with extracted free regions, and this improves the maneuverability of the mobile manipulator in narrow, cluttered spaces. Experimental results showcase significant improvements in safety, efficiency, and task completion rates. These findings underscore the robustness of the proposed method, particularly in narrow, cluttered environments where conventional approaches could falter. The open-source project can be found at https://github.com/Chunx1nZHENG/MM-with-Whole-Body-Safety-Release.git.

Data-driven decision-making processes increasingly utilize end-to-end learnable deep neural networks to render final decisions. Sometimes, the output of the forward functions in certain layers is determined by the solutions to mathematical optimization problems, leading to the emergence of differentiable optimization layers that permit gradient back-propagation. However, real-world scenarios often involve large-scale datasets and numerous constraints, presenting significant challenges. Current methods for differentiating optimization problems typically rely on implicit differentiation, which necessitates costly computations on the Jacobian matrices, resulting in low efficiency. In this paper, we introduce BPQP, a differentiable convex optimization framework designed for efficient end-to-end learning. To enhance efficiency, we reformulate the backward pass as a simplified and decoupled quadratic programming problem by leveraging the structural properties of the KKT matrix. This reformulation enables the use of first-order optimization algorithms in calculating the backward pass gradients, allowing our framework to potentially utilize any state-of-the-art solver. As solver technologies evolve, BPQP can continuously adapt and improve its efficiency. Extensive experiments on both simulated and real-world datasets demonstrate that BPQP achieves a significant improvement in efficiency--typically an order of magnitude faster in overall execution time compared to other differentiable optimization layers. Our results not only highlight the efficiency gains of BPQP but also underscore its superiority over differentiable optimization layer baselines.

Artificial Intelligence is rapidly embedding itself within militaries, economies, and societies, reshaping their very foundations. Given the depth and breadth of its consequences, it has never been more pressing to understand how to ensure that AI systems are safe, ethical, and have a positive societal impact. This book aims to provide a comprehensive approach to understanding AI risk. Our primary goals include consolidating fragmented knowledge on AI risk, increasing the precision of core ideas, and reducing barriers to entry by making content simpler and more comprehensible. The book has been designed to be accessible to readers from diverse backgrounds. You do not need to have studied AI, philosophy, or other such topics. The content is skimmable and somewhat modular, so that you can choose which chapters to read. We introduce mathematical formulas in a few places to specify claims more precisely, but readers should be able to understand the main points without these.

Sequential Decision Making under Uncertainty (SDMU) is ubiquitous in many domains such as energy, finance, and supply chains. Some SDMU applications are naturally modeled as Multistage Stochastic Optimization Problems (MSPs), but the resulting optimizations are notoriously challenging from a computational standpoint. Under assumptions of convexity and stage-wise independence of the uncertainty, the resulting optimization can be solved efficiently using Stochastic Dual Dynamic Programming (SDDP). Two-stage Linear Decision Rules (TS-LDRs) have been proposed to solve MSPs without the stage-wise independence assumption. TS-LDRs are computationally tractable, but using a policy that is a linear function of past observations is typically not suitable for non-convex environments arising, for example, in energy systems. This paper introduces a novel approach, Two-Stage General Decision Rules (TS-GDR), to generalize the policy space beyond linear functions, making them suitable for non-convex environments. TS-GDR is a self-supervised learning algorithm that trains the nonlinear decision rules using stochastic gradient descent (SGD); its forward passes solve the policy implementation optimization problems, and the backward passes leverage duality theory to obtain closed-form gradients. The effectiveness of TS-GDR is demonstrated through an instantiation using Deep Recurrent Neural Networks named Two-Stage Deep Decision Rules (TS-DDR). The method inherits the flexibility and computational performance of Deep Learning methodologies to solve SDMU problems generally tackled through large-scale optimization techniques. Applied to the Long-Term Hydrothermal Dispatch (LTHD) problem using actual power system data from Bolivia, the TS-DDR not only enhances solution quality but also significantly reduces computation times by several orders of magnitude.

New products must be formulated rapidly to succeed in the global formulated product market; however, key product indicators (KPIs) can be complex, poorly understood functions of the chemical composition and processing history. Consequently, scale-up must currently undergo expensive trial-and-error campaigns. To accelerate process flow diagram (PFD) optimisation and knowledge discovery, this work proposed a novel digital framework to automatically quantify process mechanisms by integrating symbolic regression (SR) within model-based design of experiments (MBDoE). Each iteration, SR proposed a Pareto front of interpretable mechanistic expressions, and then MBDoE designed a new experiment to discriminate between them while balancing PFD optimisation. To investigate the framework's performance, a new process model capable of simulating general formulated product synthesis was constructed to generate in-silico data for different case studies. The framework could effectively discover ground-truth process mechanisms within a few iterations, indicating its great potential for use within the general chemical industry for digital manufacturing and product innovation.

No political deepfake has alarmed the world's disinformation experts more than the doctored audio message of U.S. President Joe Biden that began circulating over the weekend. In the phone message, a voice edited to sound like Biden urged voters in New Hampshire not to cast their ballots in Tuesday's Democratic primary. "Save your vote for the November election," the phone message went. It even made use of one of Biden's signature phrases: "What a bunch of malarkey." In reality, the president isn't on the ballot in the New Hampshire race -- and voting in the primary doesn't preclude people from participating in November's election.

A 61-year-old man is suing Macy's and the parent company of Sunglass Hut over the stores' alleged use of a facial recognition system that misidentified him as the culprit behind an armed robbery and led to his wrongful arrest. While in jail, he was beaten and raped, according to his suit. Harvey Eugene Murphy Jr was accused and arrested on charges of robbing a Houston-area Sunglass Hut of thousands of dollars of merchandise in January 2022, though his attorneys say he was living in California at the time of the robbery. He was arrested on 20 October 2023, according to his lawyers. According to Murphy's lawsuit, an employee of EssilorLuxottica, Sunglass Hut's parent company, worked with its retail partner Macy's and used facial recognition software to identify Murphy as the robber.

Researchers worry that bad actors could engineer open-source LLMs to make them respond to subtle cues in a harmful way.Credit: Smail Aslanda/Anadolu Just like people, artificial-intelligence (AI) systems can be deliberately deceptive. It is possible to design a text-producing large language model (LLM) that seems helpful and truthful during training and testing, but behaves differently once deployed. And according to a study shared this month on arXiv1, attempts to detect and remove such two-faced behaviour are often useless -- and can even make the models better at hiding their true nature. The finding that trying to retrain deceptive LLMs can make the situation worse "was something that was particularly surprising to us … and potentially scary", says co-author Evan Hubinger, a computer scientist at Anthropic, an AI start-up company in San Francisco, California. Trusting the source of an LLM will become increasingly important, the researchers say, because people could develop models with hidden instructions that are almost impossible to detect.

The vine-like Filobot was inspired by plants.Credit: Del Dottore et al., Sci. Researchers have demonstrated a robot that grows like a vine in response to stimuli such as light and pressure. The machine -- named FiloBot -- has a head that prints its body by melting and extruding plastic, which then solidifies as it cools. The robot's head is connected to a base by a thin hose, through which it receives a fresh supply of plastic from a spool. FiloBot's growth rate is slow -- its body elongates by just a few millimeters each minute.

It is a truth universally acknowledged, that a single man in possession of a computer must be in want of an AI girlfriend. Certainly a lot of enterprising individuals seem to think there's a lucrative market for digital romance. OpenAI recently launched its GPT Store, where paid ChatGPT users can buy and sell customized chatbots (think Apple's app store, but for chatbots) – and the offerings include a large selection of digital girlfriends. "AI girlfriend bots are already flooding OpenAI's GPT store," a headline from Quartz, who first reported on the issue, blared on Thursday. Quartz went on to note that "the AI girlfriend bots go against OpenAI's usage policy … The company bans GPTs'dedicated to fostering romantic companionship or performing regulated activities'."